Systems and methods of backhaul optimization

ABSTRACT

Various embodiments provide for systems and methods of backhaul optimization. An exemplary system comprises a plurality of low power cells and a connector node. The connector node may be in communication with the plurality of low power cells. The connector node may be configured to receive demands from each of the plurality of low power cells. Each of the demands may indicate a demand at a predetermined time. The connector node may be further configured to determine a rate for each of the plurality of low power cells based on the demands of each of the low power cells and the assigned rate of the other of the plurality of low power cells. The connector node may be further configured to allocate capacity based on the determined rates.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.13/299,342, filed Nov. 17, 2011 and entitled “Systems and Methods ofBackhaul Optimization,” which claims priority from U.S. ProvisionalPatent Application Ser. No. 61/414,860, filed Nov. 17, 2010 and entitled“Constrained Backhaul Optimization for Self Organizing Networks,” whichare hereby incorporated by reference herein.

FIELD OF THE INVENTION(S)

The present invention(s) relate to backhaul systems, and moreparticularly, some embodiments relate to systems and methods forbackhaul optimization.

DESCRIPTION OF THE RELATED ART

In wireless cellular systems, the radio access network is often abottleneck to achieving high capacity. This bottleneck is often a resultof impairments of the wireless medium. In comparison to radio accessnetwork capacity, backhaul capacity is typically large. Further,dedicated backhaul links are assumed to provide the needed capacity tosupport backhaul requirements.

The new trend to improve cellular coverage (especially indoors) andnetwork capacity is to populate a network with low power base stations,thus adding cells of smaller radius referred to as “picocells.”Unfortunately, the increased density of picocells when compared to amacrocell network raises a number of design issues. In particular, largenumber of picocells typically require automation of the operation andoptimization of transmit parameters. This leads to the concept ofself-organizing networks (SONs). Furthermore, the presence of a largenumber of picocells impact the backhaul. For example, providing adedicated backhaul (such as fiber) to each picocell leads to a largedeployment cost. In such settings, the idea of wireless backhaul thatconnects multiple picocells becomes very attractive.

While this approach enables a more efficient way of providing backhaulto SONs and can do so at high spectral efficiency, backhaulcommunication is performed over a wireless channel shared among multiplepicocells. The channel is typically non-line-of-sight ornear-line-of-sight. Thus, depending on the channel quality, the backhaulcapacity per picocell may match or even fall below the total throughputprovided by that picocell to its mobile users. Therefore, unlikebackhaul in the macro cellular networks, in these settings the backhaulbecomes constrained. Overall, the differences of the considered backhaulfrom the dedicated backhaul are:

-   -   1) Backhaul is time-varying. Due to fading in the wireless        channel, channel gains between a connector cell (CN) and a        picocell vary in time.    -   2) No dedicated backhaul per picocell exists. The available        backhaul capacity is shared among picocells.    -   3) Interference between picocells. Forwarding data to/from one        picocell interferes with communication of other picocells.    -   4) The backhaul is constrained (i.e., it is not always unlimited        compared to the rates in the access network).

SUMMARY OF EMBODIMENTS

Various embodiments provide for systems and methods of backhauloptimization. An exemplary system comprises a plurality of low powercells and a connector node. The connector node may be in communicationwith the plurality of low power cells. The connector node may beconfigured to receive demands from each of the plurality of low powercells. Each of the demands may indicate a demand at a predeterminedtime. The connector node may be further configured to determine a ratefor each of the plurality of low power cells based on the demands ofeach of the low power cells and the assigned rate of the other of theplurality of low power cells. The connector node may be furtherconfigured to allocate capacity based on the determined rates.

In various embodiments, at least one of the low power cells is apicocell or a femtocell. The system may further comprise a core networkwherein the connector node is in further communication with the corenetwork. In some embodiments, each of the plurality of low power cellsis configured to determine demand based on current demand and expecteddemand at the predetermined time. Each of the plurality of low powercells may be configured to determine demand based on a quality ofservice of one or more users of the low power cells.

In some embodiments, the connector node is configured to determine adifference between each of the demands from the plurality of low powercells and rates for each of the low power cells, sum the differences,and determine the assigned rates based on a minimum of the sum. Thedifference between at least one of the demands from the plurality of lowpower cells and at least one of the rates may be weighted.

A channel between the connector node and at least one of the pluralityof low power cells may be near-line-of-sight. In some embodiments, theconnector node changes the predetermined time based on an observedhistory of usage of the low power cells.

An exemplary method may comprise receiving demands, by a connector nodefrom each of a plurality of low power cells, each of the demandsindicating a demand at a predetermined time, determining, by theconnector node, a rate for each of the plurality of low power cellsbased on the demands of each of the low power cells for thepredetermined time as well as the assigned rate of the other of theplurality of low power cells, and allocating capacity, by the connectornode, based on the determined rates.

An exemplary computer readable medium may comprise executableinstructions. The instructions may be executable by a processor toperform a method. The method may comprise receiving demands, by aconnector node from each of a plurality of low power cells, each of thedemands indicating a demand at a predetermined time, determining, by theconnector node, a rate for each of the plurality of low power cellsbased on the demands of each of the low power cells for thepredetermined time as well as the assigned rate of the other of theplurality of low power cells, and allocating capacity, by the connectornode, based on the determined rates

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows the considered system in which a connector node (CN)provides the backhaul connection to a set of picocells in accordancewith some embodiments.

FIG. 2 illustrates a typical rate region that can be achieved over awireless backhaul channel at two picocells in accordance with someembodiments.

FIG. 3 shows a solution to the backhaul allocation problem for specificvalues of traffic demands at two picocells, denoted (D₁, D₂) for thecase of the achievable rate region of FIG. 1 in accordance with someembodiments.

FIG. 4 presents allocated backhaul rates to two picocells as a functionof their demands (D₁, D₂), for the achievable rate region of FIG. 1 inaccordance with some embodiments.

FIG. 5 is a flowchart of a method of allocation of backhaul capacityaccording to some embodiments.

FIG. 6 shows a network of picocells grouped over regions in accordancewith some embodiments.

DETAILED DESCRIPTION OF VARIOUS EMBODIMENTS

One problem when the backhaul capacity is constrained is how to allocatethis limited resource among picocells. Backhaul allocation may determinethe rate and power allocation given to each picocell, as well astime/bandwidth allocation in case the transmission scheme does nottransmit simultaneously to all users. An exemplary method in someembodiments may provide for optimally allocating available backhaulcapacity among picocells to maximize the picocell utility. In variousembodiments, backhaul allocation exploits characteristics of thebackhaul wireless channel and also adapts to changes in user traffic atpicocells.

In various embodiments, a method for optimization of wireless backhaulsharing in cellular networks where the backhaul capacity is limited isdiscussed. This situation occurs, for example, in cellular networksdeploying low power (and hence, short coverage) base stations which maybe referred to as picocells and femtocells (e.g., low power cells). Theproposed backhaul sharing optimization can achieve different objectivesas desired by the network operator including: maximum revenue, maximumthroughput, fairness, prioritization of services in a picocell (i.e.,quality-of-service (QoS)), and prioritization of picocells. Theparameters may be collectively referred to as demands. Which of theobjectives are achieved may depend on the utility metric that aredescribed. The utility metric may also depend on the user traffic ineach picocell and it may aim to maximize utilization ofpicocell/femtocell access network.

Some embodiments allow a wireless backhaul to be shared among picocellsbased on the demands as well as conditions of the underlying wirelesschannel between the connector node (CN) providing backhaul and thepicocells/femtocells utilizing this backhaul. In various embodiments, anexemplary method to determine transmission parameters (e.g.,transmission power and backhaul rate) if allocated for each picocell atthe connector node. The optimal choice of power and rate allocation maydepend on the utility metric. The parameters may be optimized such thatpicocell utility (which may depend on demands) may be maximized. Themaximization may exploit the characteristics of the time-varyingwireless channel between the CN and picocells to increase the totalbackhaul capacity. Furthermore, as the load in the access networkchanges, the backhaul may adapt to the time-varying load. Exemplarymethods described herein may focus on communication from the CN to thepicocells, but the same methodology may apply, in some embodiments, forthe backhaul optimization to support the uplink traffic delivered fromthe picocells to the CN.

Some embodiments may apply for any physical layer transmission schemedeployed for communication between the connector node andpicocells/femtocells. The transmission scheme, power allocation and thewireless channel conditions may determine the set of backhaul rates thatcan be delivered to picocells, referred to as the achievable rateregion. These rates may be determined either theoretically orexperimentally. Unlike in the dedicated backhaul case, their property isthat, due to the shared wireless backhaul channel, there is a tradeoffbetween the backhaul rates allocated to different picocells. In someembodiments, the method may determine an operating point on the givenachievable rate region by determining allocated powers and backhaulrates for picocells, based on the above demands. Instead of consideringonly the achievable rate region, the method in some embodiments applymore generally to consideration of the achievable region that capturesrates, traffic delays, outage probabilities, etc.

The operating point may be obtained by optimization (minimization) of aproperly designed cost function. Minimization of the cost function mayresult in the maximum utility of the picocells. The required property ofthe cost function may be that, if there exists an operating point in theachievable rate region at which the demands can be fully satisfied, thatpoint is chosen as the operating point by the optimization. In thiscase, the backhaul capacity is not a constraint. Otherwise, theoperating point obtained by optimization is on the boundary of theachievable region.

Some embodiments described herein describe single CN providing backhaulto a set of picocells. However, some embodiments may readily apply to anetwork scenario in which there is multiple CNs each providing backhaulto a different set of picocells. All CNs may operate in the samebandwidth. Thus, this setting may capture the impact of interferenceintroduced by a CN at the picocells that are served by other CNs. Theeffect of interference may be captured in the achievable rate region.The achievable rate region may characterize a set of backhaul rates thatcan be delivered at all picocells in the network. Again, based ondemands for all picocells, an operating point on the region may bedetermined, thereby determining powers and rates that CNs need toallocate to picocells they serve.

Some embodiments may be used to determine an optimal sharing ofavailable spectrum between the downlink and uplink backhaul traffic,based on their traffic and demands. To accommodate changing demands inthe access network, some embodiments re-allocate backhaul in every timeinterval T_(CN) or at different time interval T_(CN). The cost functionmay depend on users' traffic in each picocell and their targeted QoS, inthe next time interval. The traffic demand for a picocell for the nextT_(CN) may be estimated based on these values. The CN may obtain thisestimate from picocells at the beginning of each interval T_(CN). Thetraffic demand may also depend on the anticipated number of newlyadmitted users in the next time interval, and the anticipated number ofterminated calls and services. These values may be estimated, forexample, based on the history of traffic and depending on the time ofthe day/week.

In various embodiments, the policy decisions about the allocated QoS foreach session of each subscriber may be determined by the Policy andCharging Rules Function (PCRF) that makes session-level policydecisions. The PCRF may provide these decisions to the Policy andCharging Enforcement Function (PCEF) located in the packet data networkgateway (PDN GW), as well as to the bearer-binding and event-reportingfunction (BBERF). Location of the BBERF may depend on the accesstechnology used. Thus, PCRF, PERF, and BBERF may have the informationrelated to the traffic demands and needed for the proposed backhauloptimization that depends on QoS. In various embodiments, thisinformation is provided to the picocells from PCRF in the form of totalrate, delay, etc. required for each QoS class in each picocell(calculated either by a picocell or by PCRF). This information may notnecessarily equal the actual traffic that arrives at the Serving Gateway(GW) to be forwarded to the picocell. Hence, communication with theserving GW can provide more precise information about the actualincoming downlink traffic. The picocell may use both information fromPCRF and the Serving GW in order to obtain a traffic estimate that maybe used in the optimization. This estimate may be then sent to the CN tobe incorporated into the cost function. For the uplink traffic,information about the number of users and their QoS may be directlyknown at a picocell, which forwards this information to the CN.

Some embodiments apply to networks in which the backhaul is providedover a wireless channel simultaneously to several base stations (e.g.,low power base stations such as picocells or femtocells). Those skilledin the art will appreciate that some embodiments described herein mayapply to microwave backhauls, backhauls with wireless nodes, cellularnetworks (e.g., 3G networks, 4G networks, LTE, WiMAX, and Wi-Fi) and thelike.

In one example, we consider a wireless network consisting of Npicocells, as shown in FIG. 1. FIG. 1 shows the considered system 100 inwhich a connector node (CN) 102 provides the backhaul connection to aset of picocells 104, 106, 108, and 110 in accordance with someembodiments. FIG. 1 depicts a picocell network with the CN 102 providingbackhaul connection to the core network 118.

The CN 102 may be any device configured to communicate with any numberof digital devices such as base stations. A digital device is any devicewith a processor or memory. For example, the CN 102 may comprise one ormore antennas configured to communicate with other nodes or cells suchas picocells, macrocells, femtocells, or any other device. The cells maybe low power cells. Those skilled in the art will appreciate that, insome embodiments, the nodes in communication with the CN 102 may be anycombination of picocells, macrocells, femtocells, and/or any otherdevice. For the purposes of describing FIG. 1, the CN 102 is depicted ascommunicating with picocells 104, 106, 108, and 110.

Each picocell may communicate with one or more digital devices such asmobile devices 112, 114, and 116. The mobile devices may be any mobilecustomer equipment, cellular communications devices, or the like. Thecore network 118 is any network that may communicate with one or moreother digital devices such as another connector node, network device(e.g., router or switch), Internet, or any other digital device.

In FIG. 1, the backhaul to the picocells is provided by the CN 102 whichcommunicates to picocells 104, 106, 108, and 110 over one or morewireless channels. Attached to each picocell 104, 106, 108, and 110 maybe a wireless transceiver for backhaul communication. Both the CN 102and the picocells 104, 106, 108, and 110 may be equipped with one ormore antennas for backhaul communication. The number of antennas at theCN 102 is denoted by M and the number of antennas at each picocell(e.g., picocell 104) is denoted by R. The channel can be line-of-sightor, typically, near-line-of-sight or non-line-of-sight. In the followingexemplary embodiment, the focus will be on the communication from the CN102 to the picocells 104, 106, 108, and 110, but the proposedmethodology of at least some embodiments described herein apply to bothcommunication directions (i.e., from/to the CN 102 to/from the picocells104, 106, 108, and 110). Each picocell may communicate with one or moremobile users 112.

The received signal y_(i) at picocell i (e.g., picocell 102) may bemodeled as:

y _(i) =H _(i) x+z _(i)   (1)

where y_(i)∈C^(Rx1), the transmitted signal at the CN 102 is x∈C^(Mx1),and the random channel gain matrix from the CN 102 to picocell i isH_(i)∈C^(RxM). Receiver noise z_(i)∈C^(Rx1) has i.i.d. components withzero mean and variance N. A block fading channel may be considered wherechannel gains are constant over the length of one code-block. In someembodiments, we assume that the channel gain matrices are known at theCN 102, which has an average transmit power constraint P.

In various embodiments, a method may apply for any physical layertransmission scheme deployed over the backhaul wireless channel (1) todeliver data to picocells 104, 106, 108, and 110. Channel (1) may beidentical to the Gaussian MIMO broadcast channel or equivalently thedownlink channel of a MIMO cellular system with no intra-cellinterference. Optimal and suboptimal transmission schemes for thischannel may include but are not limited to: dirty paper coding,superposition coding, beamforming and TDMA/FDMA transmission with orwithout channel dependent scheduling.

In the presence of fading, maximizing the sum throughput may be achievedby a simple TDMA approach in which, in each block, the transmitter maysend data only to the user with the best channel gain, in that wayexploiting multiuser diversity. The choice of a particular transmissionscheme may determine the set of rates that can be simultaneously or nearsimultaneously delivered to a plurality of picocells. An achievable setof rates is referred to as an achievable rate region for thattransmission scheme. The set of achievable rates for a specific schemeis denoted as C(P).

The choice of an operating point on the achievable rate region C(P) maybe done based on the network performance objective desired to beachieved. Such objectives include, without limitation: maximum revenue,maximum throughput, fairness, prioritization of services in a picocell(e.g., quality-of-service (QoS)) and prioritization of picocells.Optimization may be done by introducing a defined metric that capturesdesired objectives. The utility metric may also depend on user trafficin each picocell. Transmit powers and backhaul rates of each picocellmay be determined such that picocell utility is maximized. Thismaximization may exploit multiuser diversity to increase the totalbackhaul capacity. Furthermore, as the load in the access networkchanges, the backhaul may adapt to the time-varying load.

In various embodiments, we choose a specific utility metric that dependson the user traffic in the picocell.

A. Backhaul Allocation for Picocells

Dynamics in the considered network may vary on two different timescales:

1) Short time changes (due to fading)

2) Long term changes (due to load change, weather).

The CN 102 may allocate backhaul resources to track long term changes.We introduce time interval T_(CN) and may readjust the backhaulallocation every T_(CN). The T_(CN) is a predetermined time interval andmay be chosen based on observed history such as dynamics of the trafficchange (e.g., rush hour, weekend, etc.). Those skilled in the art willappreciate that the T_(CN) may be changed due to other conditions (e.g.,weather change, emergency, special events).

A backhaul rate allocated to picocell i is denoted R,(t). The demand forbackhaul at each time interval T_(CN) at a picocell i is denotedD_(i)(t). A value of D_(i)(t) is determined by picocell i based on thecurrent load of the picocell, anticipated load in the next T_(CN)interval, QoS for each user, and interference from other picocells. Apicocell (e.g., picocell 102) may report a value of D_(i)(t) everyT_(CN) (e.g., every T_(CN) seconds). We consider a solution in one timeinterval, and therefore, we can drop index t to simplify notation.

The chosen transmission scheme determines C(p) and is thus may be knownto the CN 102. After obtaining values of D_(i), i=1, . . . , N frompicocells (e.g., picocells 104, 106, 108, and 110), CN 102 may choosetransmit powers and rates for the next interval T_(CN) as follows. Thepicocell utility may be maximized based on a cost function that isinversely related to utility (maximum utility corresponds to minimumcost). The cost function may satisfy the following:

-   -   The cost function is non-increasing in assigned rate to a        picocell.    -   If (D₁, . . . , D_(N))∈C(P) then (R₁*, . . . , R_(N)*)=(D₁, . .        . , D_(N)). Otherwise, an optimal solution is on the boundary of        the achievable rate region, C(P).

Based on the above, the optimization problem may be formulated as:

$\begin{matrix}{\min {\sum\limits_{i = 1}^{N}\; {w_{i}{g_{i}\left( {D_{i},R_{i}} \right)}}}} & (2)\end{matrix}$

subject to (R₁, . . . , R_(N))∈C(P)

where

g(D,R)=(D−R)^(P).   (3)

and x₊=max {0, x}, w_(i) is the weight assigned to the cost function ofcell i, and p≧0. We denote the demand point as D=(D₁, . . . , D_(N)) andan achievable rate point as R=(R₁, . . . , R_(N)).

An alternate algorithm may be formulated by defining a simpleroptimization problem, as follows:

$\begin{matrix}{\min {\sum\limits_{i = 1}^{N}\; {w_{i}{g_{i}\left( {D_{i},R_{i}} \right)}}}} & (4)\end{matrix}$

subject to (R₁, . . . , R_(N))∈C(P)

where g(D,R)=(D−R)^(P)   (5)

Under certain, quite non-restrictive conditions, the optimization (4)may be shown to yield the optimal solution to (2).

The two algorithms thus differ only in cost functions (3) and (5). Itmay be observed that when D∈C(P), the cost function in (4) evaluated inD is zero and thus an optimal solution is D. In other words, if thedemand vector is within the achievable rate region, the cost is zero andthe backhaul is not a constraint. Otherwise, a solution to (4) may be aset of rates (R₁*, . . . R_(N)*) on the boundary of C(P). The optimalsolution determines the specific point in region C(P) and thusdetermines the power allocation that achieves this point, and that theCN 102 should use.

Consider next the case p=2, w_(i)=1, for all i. In this case, the costfunction minimizes the squared distance between the demand point D and arate point R in the achievable rate region C(p). For the broadcastchannel of FIG. 2, the solution of (4) is shown in FIG. 3 for a specificchoice of demands (D₁,D₂). FIG. 2 illustrates a typical rate region thatcan be achieved over a wireless backhaul channel with two picocells inaccordance with some embodiments. The typical rate region is bounded byboundary 202. FIG. 2 depicts a capacity region 200 of a two userbroadcast channel with p=10.

FIG. 3 shows a solution 300 to the backhaul allocation problem forspecific values of traffic demands at two picocells, denoted (D₁, D₂) atpoint 304 for the case of the achievable rate region of FIG. 1 inaccordance with some embodiments. Optimal rates are denoted (R₁*, R₂*)at point 306 and are on the boundary 302 of the achievable rate region.In the example of FIG. 3, backhaul allocation is depicted for twopicocells with demands D₁=4, D₂=3. The closest point along the boundary302 of available rates (i.e., backhaul allocation) is the point closestto (D₁, D₂) 304 (e.g., (3, 4)) which is point 306 (R₁*, R₂*).

Hence the allocation (2) performs the following:

At each time interval T_(CN), if D_(i)∈C(P) the solution assignsR_(i)*=D_(i). Otherwise: it chooses (P₁, . . . , P_(N)) such that (R₁*,. . . , R_(N)*)∈C(P) and R* is the closest point to D on the capacityregion boundary.

FIG. 4 presents allocated backhaul rates 400 to two picocells as afunction of their demands (D₁, D₂), for the achievable rate region ofFIG. 1 in accordance with some embodiments. In this figure, demands arechosen so that D₁=D₂. It may be observed that for small values of D₁ andD₂ when (D₁, D₂)∈C(P), the allocated rates equal the demand, i.e., (R₁*,R₂*)=(D₁, D₂). As D₁ and D₂ increase, the allocated rates cannot matchdemand anymore at point 402. Picocell 2 is penalized more than picocell1 because the channel to picocell 1 is better than to picocell 2 (thiscan be observed from FIG. 2). Thus, the allocation (4) assigns ratesbased on both demands and wireless channel characteristics, as was ourgoal.

We also allow for different values of p (the penalty parameter). Thisenables the algorithm to control the penalty based on demand. Forexample, for larger values of p, picocells with higher demand may bepenalized more, and vice versa. This ensures some level of fairness inresource allocation. If no demands are reported by picocells in intervalT_(CN), the backhaul can be chosen: to maximize total capacity, equalfor all picocells, based on the classes of picocells, etc.

Instead of metrics presented in (2) and (4), other metrics can beconsidered such as: 1) min max |D−R|, 2) maximizing picocell rates suchthat all picocells satisfy a minimum rate requirement, 3) minimizing thenumber of picocells whose rate requirements are not met, etc. The nextsection proposes an approach based on a metric that performs backhauloptimization for a group of picocells in a region.

FIG. 5 is a flowchart 500 of a method of allocation of backhaul capacityaccording to some embodiments. In step 502, each of a plurality of basestations determines demand. Demand may be based on current demands byone or more users and expected demand for a predetermined time (e.g.,T_(CN)). Demand may also be based on interference between base stationsand/or quality of service (QoS) for one or more users. The base stationsmay be any digital device such as a picocell or femtocell.

In step 504, each base station provides the determined demands to theconnector node over a wireless network. In some embodiments, each basestation comprises a module that determines demands and/or provides thedemands to the connector node. The module may be any hardware orsoftware. In some embodiments, the base station may comprise aprocessor, memory, and computer readable medium that stores executableinstructions. The computer readable medium is any medium, such as a harddrive, compact disk, solid state drive, or the like configured to storedata. The computer readable medium may be nontransitory. Theinstructions may be executable by a processor to determine demand andprovide the demand to the connector node. In some embodiments, theconnector node requests demands from one or more base stations. Invarious embodiments, the base stations provide the demands to theconnector node without waiting for a request.

In some embodiments, the demand provided by the base stations to theconnector node represents a single value for each base station. In otherembodiments, the demand provided by each base station may includeadditional information such as quality of service demands or otherinformation.

In step 506, the overall backhaul capacity for the plurality of basestations is determined. The overall backhaul capacity may be determinedbased on past communication between any number of base stations and thecentral node. The overall backhaul capacity may be based, in part, onthe effects of weather on the rate of service between one or more basestations and the central node, events, time of day, time of year, or thelike. The determination of backhaul capacity may be updated at any timeto take into account current conditions (e.g., availability of basestations, extreme weather, or the like).

In step 508, penalties for allocation of resources and weight for eachbase station may be determined. Allocation of resources may berepresented by exponent p in equations 3 and 5. The penalty parameter pmay be selected based on the difference between demand and availablerates for a base station. The penalty parameter may be different fordifferent base stations and/or may be updated at any time.

The weight per base station may be represented by exponent w inequations 3 and 5. Any number of base stations may have the same ordifferent weights. The determination of a weight may be influenced basedon QoS of one or more users or dataflows associated with the basestation or any other base station.

In step 510, a rate for each base station is determined. The rate for abase station may be based on demands of a plurality of base stations,backhaul capacity, and the rates allocated to other base stations of theplurality of base stations. In one example, a summation is generatedbased on all base stations. For each base station, assuming demand isgreater than available rate, the difference between demand and a rate tobe determined is identified and the difference penalized by the penaltyparameter (exponent p) (e.g., see equation 5). Each penalized differenceis multiplied by a weight for the associated base station (e.g., see win equation 4). The results are added together to create an equationwith a set of unknown rates (i.e., one unknown rate for each basestation). A minimum is used to determine the optimal result (e.g.,shortest distance between an available rate(s) and demand(s)) todetermine rates for each of the base stations.

In step 512, the CN node allocates backhaul capacity based on thedetermined rate.

In various embodiments, the CN node, like the base station, may compriseone or more modules to perform one or more functions. The modules may behardware or software. In various embodiments, the CN node comprises aprocessor, memory, and a computer readable medium comprising executableinstructions to perform all or some of the steps in FIG. 5.

B. Backhaul Allocation Over Regions

Some embodiments may be generalized to allow optimization of the totalbackhaul allocated to several picocells. This generalization may bereferred to as backhaul allocation over regions. After the backhaul rateand power allocation have been performed at the CN, there may be a setof picocells for which the demand has not been met (i.e., R_(i)<D_(i)).These picocells may not be able to support their users nor admit newones. Hence, the picocells may attempt to handoff some of their users tothe neighboring cells. Based on demands and channel conditions, thebackhaul allocation may result in a scenario in which the demand was notmet for a group of neighboring picocells without handoffs oralternatives.

We denote the set of all picocells {1, . . . , N} as V. We let S_(i)∈Vdenote a subset of V and consider {S₁, . . . , S_(M)} for some M>0. Forbackhaul allocation, we then consider the following generalization ofproblem (2):

$\begin{matrix}{\min {\sum\limits_{a = 1}^{M}\; {w_{a}{g\left( {D_{a},R_{a}} \right)}}}} & (6)\end{matrix}$

subject to (R₁, . . . , R_(N))∈C(P)

$R_{a} = {\sum\limits_{i \in s_{a}}^{\;}\; R_{i}}$

where g(D,R) is given by (3).

R_(a) is the sum backhaul rate allocated to the set of picocells S_(a).We choose S_(a) depending on the size and shape of regions over which wewant to jointly allocate backhaul: for M=N, S₁={1}, S₂={2}, . . . ,S_(N)={N}, problem (6) reduces to (2). More generally, to avoid backhaulallocation that does not meet demand in a certain region, we can chooseS_(a) to be a set of neighboring nodes. The solution determines the sumrate R_(a) for the set S_(a). An example is shown in FIG. 6. FIG. 6shows a network of picocells grouped over regions in accordance withsome embodiments. In this example, there are four neighboring cells ineach region. The proposed broadcast allocation approach is alsogeneralized to allow backhaul optimization over regions.

In addition to long term changes, the assigned backhaul capacityexperiences fast variations due to fading within each interval T_(TN).We point out that a network may not attempt to adapt to these changes.These changes may be handled by the physical layer transmission schemeand are therefore already taken into account in the achievable rateregion C(P).

C. Generalization to Other Scenarios

Allocation of backhaul delivered for the downlink of the cellular systemhas been discussed regarding some embodiments herein. Backhaul can alsobe optimized when delivering uplink traffic from picocells to the CN.The channel from picocells to the CN is a multiple-access channel (MAC),and the optimal power allocation in fading is known. In a dual manner tothe broadcast channel, for the fading MAC, multiuser diversity maximizesthe total throughput. A similar methodology for the backhaul allocationfor traffic from picocells to the CN may be applied. In this case, asolution to the optimization of backhaul allocation may determinetransmit powers at picocells as well as the backhaul capacity availablefor each of them.

Furthermore, some embodiments described herein may be used to determinethe optimal sharing of available spectrum between the downlink anduplink backhaul traffic, based on their traffic and demands.Furthermore, some embodiments may readily apply to a network scenario inwhich there are multiple CNs each providing backhaul to a different setof picocells. In some embodiments, all CNs may operate in the samebandwidth. Thus, this setting captures the impact of interferenceintroduced by a CN at the picocells that are served by other CNs. In atleast one approach, the effect of interference may be captured in theachievable rate region. The achievable rate region may characterize aset of backhaul rates that may be delivered at all picocells in thenetwork. It may also capture the interference management scheme deployedat the CN. Again, based on demands for all picocells, an operating pointon the region may be determined, thereby determining powers and ratesthat CNs may need to allocate to picocells they serve.

Various embodiments are described herein as examples. It will beapparent to those skilled in the art that various modifications may bemade and other embodiments can be used without departing from thebroader scope of the present invention. Therefore, these and othervariations upon the exemplary embodiments are intended to be covered bythe present invention.

1. A system comprising: a processor; and a computer readable mediumincluding executable instructions executable by the processor to performa method comprising: receiving a set of demands from each of a pluralityof low power cells, each of the set of demands being reported by andindicating a desired parameter for one of the plurality of low powercells at a predetermined time; and determining a particular backhaulrate for each of the plurality of low power cells based on the set ofdemands received and a set of assigned backhaul rates associated withthe plurality of low power cells.
 2. The system of claim 1, wherein atleast one of the plurality of low power cells is a picocell.
 3. Thesystem of claim 1, wherein at least one of the plurality of low powercells is a femtocell.
 4. The system of claim 1, further comprising acore network wherein the system is in communication with the corenetwork.
 5. The system of claim 1, wherein each of the plurality of lowpower cells is configured to determine a given demand based on a currentdemand and an expected demand at the predetermined time.
 6. The systemof claim 1, wherein each of the plurality of low power cells isconfigured to determine a given demand based on a quality of service ofone or more users of the plurality of low power cells.
 7. The system ofclaim 1, wherein the determining the particular backhaul rate for eachof the plurality of low power cells comprises determining a differencebetween each of the set of demands and each of the set of assignedbackhaul rates, summing the differences, and determining the particularbackhaul rates based on a minimum of the sum.
 8. The system of claim 7,wherein the difference between at least one of the set of demands and atleast one of the set of assigned backhaul rates is weighted.
 9. Thesystem of claim 1, wherein a channel between the system and at least oneof the plurality of low power cells is near-line-of-sight.
 10. Thesystem of claim 1, wherein the method further comprises changing thepredetermined time based on an observed history of usage of theplurality of low power cells.
 11. A system comprising: means forreceiving demands from each of a plurality of low power cells, each ofthe demands indicating a demand at a predetermined time; means fordetermining a rate for each of the plurality of low power cells based onthe demands of each of the plurality of low power cells for thepredetermined time as well as an assigned rate of the other of theplurality of low power cells; and means for allocating capacity based onthe determined rates.
 12. The system of claim 11, wherein at least oneof the plurality of low power cells is a picocell.
 13. The system ofclaim 11, wherein at least one of the plurality of low power cells is afemtocell.
 14. The system of claim 11, further comprising means forpassing information from at least one of the plurality of low powercells to a core network.
 15. The system of claim 11, further comprisingmeans for determining demand at each of the plurality of low power cellsbased on a current demand and an expected demand at the predeterminedtime.
 16. The system of claim 11, further comprising means fordetermining demand at each of the plurality of low power cells based ona quality of service of one or more users of the plurality of low powercells.
 17. The system of claim 11, wherein the means for determining theparticular backhaul rate for each of the plurality of low power cellscomprises means for determining a difference between each of the set ofdemands and each of the set of assigned backhaul rates, summing thedifferences, and determining the particular backhaul rates based on aminimum of the sum.
 18. The system of claim 17, wherein the differencebetween at least one of the set of demands and at least one of the setof assigned backhaul rates is weighted.
 19. The system of claim 11,wherein a channel between the system and at least one of the pluralityof low power cells is near-line-of-sight.
 20. The system of claim 11,further comprising means for changing the predetermined time based on anobserved history of usage of the plurality of low power cells.